Josh Haimson (@Just_Joshing) header
Josh Haimson (@Just_Joshing) profile picture

Co-founder @Inductive_Bio via @FlatironHealth @MIT_CSAIL | occasional takes on ai/ml, life sciences, music, & nerd puns... but mostly retweets

New York, NY
Joined June 2012
Photo by Josh Haimson (@Just_Joshing)Photo by Josh Haimson (@Just_Joshing)Photo by Josh Haimson (@Just_Joshing)Photo by Josh Haimson (@Just_Joshing)Photo by Josh Haimson (@Just_Joshing)Photo by Josh Haimson (@Just_Joshing)Photo by Josh Haimson (@Just_Joshing)Photo by Josh Haimson (@Just_Joshing)Photo by Josh Haimson (@Just_Joshing)
Josh Haimson retweeted
To celebrate our recent launches, we are hosting gatherings in San Francisco (October 6) and New York (October 13). We'll be bringing together folks from the broader AI and drug discovery community for snacks, drinks, and (in New York, at least) a robot in a fume hood. It's gonna be great -- RSVP before space fills up! SF: luma.com/6ly6k2xk NYC: luma.com/tdenkxbw
Today @inductive_bio is launching Beacon-2, which can predict human dose directly from chemical structure. A dirty secret of AI for drug discovery is that most systems optimize the wrong reward, making them near useless in practice. Human dose is the right reward – it’s what every drug program is already optimizing. Making it computable will unlock the real-world impact of agentic drug discovery. Beacon-2 builds on our Beacon-1 models that beat 750+ competitors to win all three OpenADMET blind challenges. Whereas Beacon-1 provided a suite of models for individual molecular properties, Beacon-2 is a single system that predicts human dose end-to-end. It has three configurable modules: an ADMET-PK foundation model, a potency module that can use our fine-tunable co-folding affinity model or physics-based methods like FEP, and a PK/PD model with physiologically-informed mechanistic models of pharmacokinetics. We share validation results from Beacon-2 in a blog we are releasing today. We show that across 20 real-world drug programs running through Inductive, dose predictions from Beacon-2 agree with those from experimental data with a Spearman’s ρ of 0.61. And in a similar study on 325 publicly available compounds from the ExpansionRx OpenADMET competition, we show a Spearman ρ of 0.79. As a proof-of-concept, we gave Beacon-2 to Indy, our medicinal chemistry agent, which used it over 5 autonomous design cycles on a recently disclosed SARS-CoV-2 program to cut predicted human dose 17x, which can be the difference between a drug program that gets killed and that makes it to clinical trials. We compared the compounds that Indy designed using Beacon-2 against compounds from an optimization loop that used a potency-only reward function. The Beacon-2 compounds were favorable across three independent measures: they were preferred by chemists in blinded comparisons 87% of the time; were more druglike according to QED; and had a lower projected dose when computed by alternate methods relying on in vivo rat models and allometric scaling. Based on these encouraging results, we are now running prospective studies where compounds optimized by Beacon-2 will be validated in the lab. Read the full post here: inductive.bio/news/beacon-2 At Inductive, our long-term goal is to build superhuman chemical intelligence, and by completing the reward function for autonomous drug discovery, Beacon-2 is an exciting step toward that goal. Reach out if you’d like to get involved.
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Big week at @inductive_bio with the launch of our agent Indy on Monday and our new model Beacon-2 today. These launches are important milestones on our journey to building superhuman chemical intelligence.
Today @inductive_bio is launching Beacon-2, which can predict human dose directly from chemical structure. A dirty secret of AI for drug discovery is that most systems optimize the wrong reward, making them near useless in practice. Human dose is the right reward – it’s what every drug program is already optimizing. Making it computable will unlock the real-world impact of agentic drug discovery. Beacon-2 builds on our Beacon-1 models that beat 750+ competitors to win all three OpenADMET blind challenges. Whereas Beacon-1 provided a suite of models for individual molecular properties, Beacon-2 is a single system that predicts human dose end-to-end. It has three configurable modules: an ADMET-PK foundation model, a potency module that can use our fine-tunable co-folding affinity model or physics-based methods like FEP, and a PK/PD model with physiologically-informed mechanistic models of pharmacokinetics. We share validation results from Beacon-2 in a blog we are releasing today. We show that across 20 real-world drug programs running through Inductive, dose predictions from Beacon-2 agree with those from experimental data with a Spearman’s ρ of 0.61. And in a similar study on 325 publicly available compounds from the ExpansionRx OpenADMET competition, we show a Spearman ρ of 0.79. As a proof-of-concept, we gave Beacon-2 to Indy, our medicinal chemistry agent, which used it over 5 autonomous design cycles on a recently disclosed SARS-CoV-2 program to cut predicted human dose 17x, which can be the difference between a drug program that gets killed and that makes it to clinical trials. We compared the compounds that Indy designed using Beacon-2 against compounds from an optimization loop that used a potency-only reward function. The Beacon-2 compounds were favorable across three independent measures: they were preferred by chemists in blinded comparisons 87% of the time; were more druglike according to QED; and had a lower projected dose when computed by alternate methods relying on in vivo rat models and allometric scaling. Based on these encouraging results, we are now running prospective studies where compounds optimized by Beacon-2 will be validated in the lab. Read the full post here: inductive.bio/news/beacon-2 At Inductive, our long-term goal is to build superhuman chemical intelligence, and by completing the reward function for autonomous drug discovery, Beacon-2 is an exciting step toward that goal. Reach out if you’d like to get involved.
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Josh Haimson retweeted
Today @inductive_bio is launching Beacon-2, which can predict human dose directly from chemical structure. A dirty secret of AI for drug discovery is that most systems optimize the wrong reward, making them near useless in practice. Human dose is the right reward – it’s what every drug program is already optimizing. Making it computable will unlock the real-world impact of agentic drug discovery. Beacon-2 builds on our Beacon-1 models that beat 750+ competitors to win all three OpenADMET blind challenges. Whereas Beacon-1 provided a suite of models for individual molecular properties, Beacon-2 is a single system that predicts human dose end-to-end. It has three configurable modules: an ADMET-PK foundation model, a potency module that can use our fine-tunable co-folding affinity model or physics-based methods like FEP, and a PK/PD model with physiologically-informed mechanistic models of pharmacokinetics. We share validation results from Beacon-2 in a blog we are releasing today. We show that across 20 real-world drug programs running through Inductive, dose predictions from Beacon-2 agree with those from experimental data with a Spearman’s ρ of 0.61. And in a similar study on 325 publicly available compounds from the ExpansionRx OpenADMET competition, we show a Spearman ρ of 0.79. As a proof-of-concept, we gave Beacon-2 to Indy, our medicinal chemistry agent, which used it over 5 autonomous design cycles on a recently disclosed SARS-CoV-2 program to cut predicted human dose 17x, which can be the difference between a drug program that gets killed and that makes it to clinical trials. We compared the compounds that Indy designed using Beacon-2 against compounds from an optimization loop that used a potency-only reward function. The Beacon-2 compounds were favorable across three independent measures: they were preferred by chemists in blinded comparisons 87% of the time; were more druglike according to QED; and had a lower projected dose when computed by alternate methods relying on in vivo rat models and allometric scaling. Based on these encouraging results, we are now running prospective studies where compounds optimized by Beacon-2 will be validated in the lab. Read the full post here: inductive.bio/news/beacon-2 At Inductive, our long-term goal is to build superhuman chemical intelligence, and by completing the reward function for autonomous drug discovery, Beacon-2 is an exciting step toward that goal. Reach out if you’d like to get involved.
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Today we’re launching Indy, @inductive_bio's medicinal chemistry assistant. Originally built for our internal team, Indy learned from some of the industry’s best drug hunters how to best support real programs and is now doubling our team's’ capacity: inductive.bio/blog/meet-indy
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Josh Haimson retweeted
We're putting our new coworker to work. Still needs some more training (and a name).
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Big shout-out to William Withering and the digitalis plant, thanks for saving my life
Nice article sharing how @Just_Joshing became so interested in drug discovery. I'm very grateful for the science from hundreds of years ago that helped my cofounder grow out of an infant heart condition!
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Great to see @inductive_bio featured in @drughunter_com! AI's biggest opportunity isn't superhuman compound design. It's doing for med chem what it's done for coding: increasing leverage across thousands of small decisions that add up to program success. drughunter.com/articles/beyo…
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Josh Haimson retweeted
Excited to welcome the newest member of our New York office.
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Josh Haimson retweeted
🌍 Just wrapped at AGI Summit—happening live now in San Francisco! “World Positive AI: Building for Human Flourishing” brought together @VishalVasishth of @obviousvc, @Just_Joshing of @inductive_bio , and Joel Edwards of @ZanskarGeo. Stay tuned for more live from the summit. 🔴 #AGISummit #AI #HumanFlourishing #WorldPositiveAI
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Really interesting and suprising results from some of our experiments with #LLMs for #Chemistry
LLMs’ impact in generative chemistry has been limited to date. We show that a slight change in how you ask the question, simply by switching the input from SMILES to IUPAC, can reduce invalid molecules by >90%. Learn more: inductive.bio/blog/molecular… #GenerativeChemistry #AI #ML
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Josh Haimson retweeted
High value AI application: Predicting properties of new drug candidates. Congrats to the Nested Tx and Inductive Bio teams on this successful collaboration showing how new AI models can predict ADME properties and accelerate small molecule drug discovery.
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With all of the excitement around #AlphaFold3, our team at @inductive_bio has been diving deep to understand where these advances might be useful for #AI enabled small-molecule drug discovery in practice. Check out our latest blog to learn more: inductive.bio/blog/strong-ba…
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I’m excited to start sharing more publicly what I’ve been working on for the last couple of years! At Inductive, we're focused on delivering better therapies to patients, faster, by accelerating compound optimization with fit-for-purpose datasets and ML #ML #AI #DrugDiscovery
We are thrilled to be coming out of stealth! See our press release (prnewswire.com/news-releases…), along with some coverage from STAT in their Readout Newsletter (statnews.com/2023/12/13/biot…).
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Extracting meaningful clinical detail from EHRs for millions of patients with cancer is challenging. @FlatironHealth uses #NLP & #ML to extract key information from unstructured documents in the curation of high quality #RWD. Read more on our approach: flatiron.com/resources/appro…
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Finally got around to reading this upcoming @iclr_conf paper by @WenhaoGao1 @rociomer3 and @cwcoley: arxiv.org/abs/2110.06389. I really liked this pragmatic yet principled approach to generative chemistry, and I highly recommend the full paper. Here’s my own summary:
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This list is awesome! Very cool to see the ways in which #ML and #AI is being used to discover new drugs
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What are y’all doing tomorrow? Asking for a friend.
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I couldn't resist. With contributions from @maxhkw
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